Two-dimensional image splicing method based on optical grating positioning in automatic visual inspection

Abstract

The invention belongs to the field of image automatic inspection and particularly discloses a two-dimensional image splicing method based on optical grating positioning in automatic visual inspection. The splicing method includes the steps of modifying the coordinate values of sub-images in a large image according to the coordinate included angle parameter of a shooting platform and a camera, calculating the position coordinates of four corners of each sub-image, obtaining the size of the large image through the actual physical distances corresponding to all pixels, calculating the pixel gray levels in sub pixel coordinate positions of the sub-images through bilinear interpolations to obtain the spliced large image, conducting modification to eliminate errors caused by inconformity of shooting coordinates through the coordinate included angle parameter of the shooting platform and the camera, and directly conducting calculation through the coordinate positions of the sub-images in the splicing process at the same time. Therefore, splicing errors caused by a splicing method where image features are used for inspect splicing are avoided, meanwhile, sub pixel accuracy is adopted for all the calculated coordinates, and the quality of the spliced image is improved.

Description

Technical field: [0001] The invention belongs to the field of automatic image detection, and specifically discloses a two-dimensional image mosaic method based on raster positioning in automatic visual detection. Background technique: [0002] In industrial production, it is necessary to inspect the surface of devices with textured features. Since the surface texture features of the inspected device are relatively small and cannot be distinguished by the naked eye, it is necessary to collect images by taking pictures, and then use the images for inspection. In order to ensure the accuracy of surface texture detection, it is necessary to take high-precision images. When the inspected device is large in size, the camera cannot capture all the device surfaces in one image. It is necessary to take multiple sub-images of the surface of the inspected device, and then form a complete large image through image stitching for inspection. When the surface of the detected device has ...

AI Technical Summary

Problems solved by technology

When the surface of the detected device has repeated texture features, the current splicing method is prone to overlap of local features, resulting in splicing errors, which affect the detection accuracy

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